Customer Equity Accelerator Podcast

Ep. 68 | Becoming the Data Ambassador



"What makes a good data ambassador? Someone who asks a lot of questions and wants to understand how data can help serve the customer." - Megan Kohout


This week Megan Kohout, VP of Ecommerce and Customer Analytics at fashion brand Kendra Scott joins Allison Hartsoe in the Accelerator. Megan shares her experiences learning to communicate the value of data to her colleagues. It wasn’t always successful but over time she learned how to present solutions that would gently call out preconceived notions while encouraging action. As a result, her internal credibility soared. Now she shares her insights with you.   

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If you want clearer insights from your data, check out how we can help:
Ep. 69 | How to Set-up a Testing Program Ep. 67 | Deepening the Customer Experience

Show Transcript

Alison Hartsoe: [00:01] This is the Customer Equity Accelerator. If you are a marketing executive who wants to deliver bottom-line impact by identifying and connecting with revenue generating customers, then this is the show for you. I'm your host, Allison Hartsoe, CEO of Ambition Data. Each week I bring you the leaders behind the customer-centric revolution who share their expert advice. Are you ready to accelerate? Then let's go! Welcome everyone. Today's show is about becoming the data ambassador, and to help me discuss this topic is Megan Kahoot. Megan is the VP of e-commerce and customer analytics at fashion and jewelry lifestyle brand at Kendra Scott, which is just an awesome startup and I'm so excited to have you tell us more about that. Megan, welcome to the show.


Megan Kohout: [00:55] Hi, thanks so much for having me.


Alison Hartsoe: [00:56] How can you tell us a little bit more about maybe first your background, and then can you tell us a little bit about Kendra Scott and how that brand has evolved, how that story has come along?


Megan Kohout: [01:08] Absolutely! So, I started working in customer analytics over a decade ago as a database analyst at Ann Taylor. When I started at Ann Taylor, the team hadn't really dug into any of the customer base transactional data that was in our CRM system, so it was my job basically to come in and start doing that, and what was really fun about that is that I could look at anything I wanted to. Identifying bundles that customers bought together over time, understanding the customer life cycle, looking at cross channel shopping, and this was where in my career I learned to really focus on the customer. Eventually, I branched out into other areas like direct mail, email, digital marketing, and eventually running an entire e-commerce business. It's all happened for me at fashion companies, but really the common connector has been understanding the customer journey and using that to guide my decisions.


Alison Hartsoe: [01:59] Now, I'm imagining that when you first got hold of the data, it might not have been complete or it might not have been pretty. Is that indeed the case when you started?


Megan Kohout: [02:09] I would say base on the case that every single company that I've been at that I've done this, no, I definitely had started. They tended in my career to join companies where I'm kind of the first person looking at the data, so that might be a little bit more why that's the issue, but generally speaking, even with some messy data, you can still learn a lot about your customers.


Alison Hartsoe: [02:31] I love what you just said because that has always been a fundamental tenant. As long as I've been analytic, it's always about work with what you have, even if it's directional, even if it's just a start, and I think people get hung up on that a lot.


Megan Kohout: [02:43] Absolutely. There's always going to be a little bit of variance between what you see in customer transaction data versus looking at overall company financials, and you have to just be a little bit accepting of that. But, I always say like one of the biggest issues with retail data is that you only get data for people who give an email address in stores. So there's going to be a portion, maybe 20 to 30% of your customers that you just won't have information for. But if you sell things for the 80% of people that you do have data for, your likely solving it for the 20% that aren't in your database.


Alison Hartsoe: [03:15] I love that. Great benchmarks. So tell us a little bit about Kendra Scott and the story of that brand.


Megan Kohout: [03:22] Absolutely. So Kendra Scott is a fashion, jewelry and lifestyle brand based out of Austin, Texas. The brand started about 16 years ago when Kendra started making jewelry for her friends and her family in her home. She was really trying to fill a gap in the market for high-quality fashion jewelry that was made out of real stones and real metals. She began signed the product in Boutiques in Austin, and eventually into department stores, and then later her own stores and her own website. Now we have 90 stores across the country. The company's valued at $1 billion and we've been growing rapidly.


Alison Hartsoe: [03:55] These pieces of jewelry. I actually went into the New York store when I was out in New York in October at the Custora conference, and I was amazed. I mean, they're beautiful pieces, but they're also fairly big. Is that because of the Texas roots?


Megan Kohout: [04:12] I do think that that might have a part of it. We do a lot of work where we look at our customers and what our customers shopping across different regions, and there's definitely regionality to what customers are buying, and the Texas women does like bigger earrings.


Alison Hartsoe: [04:24] Everything's bigger in Texas, right? Okay, so this is like a rocket ship, right? It's a big journey that you've been on with this brand. Tell us a little bit about why I should care about becoming the ambassador of data when the company is taking off, and things are going really well. Don't people just eat up every piece of information you put in front of them?


Megan Kohout: [04:49] They do. What happens that a lot of companies when they're in that early stage, and I found that across the board is that people have a lot of assumptions about who the customer is and how they shop, and sometimes those assumptions are correct, and so being that data ambassador is a really, really great role to be in. And that's if you're at a very junior level or if you are at senior level, because you just get to listen and absorb and hear the things that people are saying about the customer, and then go in, dig in, and find out what's really happening, and then share that back out and say, now that we know this, we should change x, Y, and z about how we're approaching our customers.


Alison Hartsoe: [05:26] And the way you've described that especially the ability to do that as either a junior or a senior level, are you essentially like being the conduit of the customer's voice and the customer information? Or is it more like you're being the conduit of specific slices of analytics information?


Megan Kohout: [05:48] I think that to do the customer analytics, the data ambassador role well, you have to put yourself into the customer's shoes. So if you're just looking at it as, I'm trying to understand what percentage of my customers purchased one time, but you're not really thinking about what that means in the larger scope of the customer, and eventually trying to take the data and then get some qualitative information through focus groups. So through surveys, you're not going to be successful in helping the organization understand the customer. So, I do think it's really important to really think about it as more than just taking slices of data.


Alison Hartsoe: [06:23] I like that. I often times think of it as the data is, so jargon loaded, and we shouldn't be asking people to understand this technical definition. The more we can make it human and relatable, perhaps the easier it is for people to absorb. Would you agree?


Megan Kohout: [06:39] Oh absolutely. It's that fun in my career as I've gone from being that junior folk to the person leading the team to help train team members to think that way. So it's like, here's what the data says and you do have to be really specific and clear about, I pulled this data this way, and this is exactly what I'm saying with this data. But you kind of take that definition and then you're like, so what is this mean for someone who isn't as much from the data? What is the user-friendly term and user-friendly ways that I can explain this to folks? You really have to take it just from one end and move it into the other.


Alison Hartsoe: [07:12] Oh yeah. So there are two pieces I want to pick up on. One is I want to hear more about your story of like how you've moved along from junior to senior, and maybe there's a particular example you can share along the way. But the other thing I want to circle too before we jump into that is this assumption that if I just put the data together, the insights will just fall out. Do you think people have that assumption typically, and what do you do about it?


Megan Kohout: [07:37] I think that sometimes people think that the data just falls out in general, and don't realize it's not just a report, but it's about how are you asking the question so you can write a query to actually answer that specific question. If the data has never in that particular form. And a lot of really simple analytics tools that are kind of drag and drop aren't going to get you the answers to the questions that you're really looking at. So in analytics those that really important part about identifying the question, and then having someone who can understand that question and figure out how do you manipulate the data and a really creative way to do that. This is one of the things where I'm a huge proponent of hiring people who understand how to write sequel because at the end of the day you need someone who can really manipulate the data, and you just can't do that with a lot of the friendly user interfaces out there.


Alison Hartsoe: [08:29] Oh, that's pretty interesting. I had heard the whole, you know, having people with sequel maybe more than five years ago, but I was actually thinking that we had evolved, and the tools were getting stronger where you didn't need that sequel piece, but what you're saying is there's still plenty of legacy tools out there that really require this kind of deep manipulation, yes?


Megan Kohout: [08:50] I think so. And maybe I'm a little bit old school that way because I've always been super, super hands-on in the data. But when you're doing work, when you're saying what customers who bought now are doing, what were they doing six months before that? That requires a lot of like flagging data, running regressions, kind of doing stuff with it that really is kind of beyond what I've seen a lot of interfaces.


Alison Hartsoe: [09:15] I know exactly what you mean because we were just looking at these in another tool, and they had three different ways that they class the data, but the definition of that was not precise. So being able to understand well how was that bill? You basically had to unpack the tool to get to the root of the data. I think that's more or less what you're getting to here is you have to have that precision.


Megan Kohout: [09:41] Exactly! That you will really need to be able to define things yourself, and I think about things where the data will be shown, in one way and maybe you just want to say, mark something as yes or no. You need to be able to write that sequel in there so then you can say this is the newer existing customer really easily, and then you can break out the data by that pivot. It's just having that flexibility to add in case when to make variables that are zero or one is so valuable.


Alison Hartsoe: [10:02] And they probably change dynamically over time. So you can set all kinds of roles around that. Okay, so we're geeking out here a little bit. Let's get back to the data ambassador. Is there an example of where maybe the way a junior person might present data and some things that they could benefit from or learn from in their particular role in their part of the organization.


Megan Kohout: [10:32] Just when you're first sharing data to a company, basic stuff can be so interesting to folks. And I'm going to share a piece of data that I'm sure it's probably true of most of the retailers out there, but even looking at the percentage of customers who only buy from you one time, it's a large percentage, it's been a large percentage of that every single three time. But having that piece of information and sharing that, and then that can help guide how you communicate with those folks. How do you make sure you have triggers set up that I know that people come back after 90 days, and a lot of people only shop one, so how do I make sure that I'm having communication at that point where they would come back at their should. So, I think that those kinds of basic metrics can help guide action very, very quickly and being able to do that can help you really kind of think your claim as someone who can help guide decisions within the organization.


Megan Kohout: [11:25] I think the other thing that's really important is just listening. So if I think that's about my time at Ann Taylor, one of the assumptions we always heard was that how we would get her to buy an additional unit was to put jewelry on our guests in our stores because that will help her complete the full outfit. But when we actually looked at the data, no matter what the first item she bought was, the next item she would buy would be a top. So that can really change how you came yourself to speak to a customer. So instead of putting a necklace on the customer, you might say, hey with those pants, pairs another top that would look really fantastic with them. So I think that it's identifying questions that people are already talking about, and then finding out what's really happening with those because people are already kind of primed to listen to information about this topic.


Alison Hartsoe: [12:15] I love that because it's such a great example of arming the front line. Many analysts suffer from the problem of, you know, I've got all these insights, or I've got all these recommendations, but people don't do anything with it. But what you're saying is look for those questions or identify those questions that people are already wrestling with a way that they can immediately use the information you have. Great. You know, I've heard that with larger companies as well, you know from CEO levels where they talk about there are plenty of business problems to think about. So if I flipped from being a junior person to being a more senior person, just going to go from one extreme to another. Is it that the same principles apply, but the problems are more complex?


Megan Kohout: [13:02] I don't know that the problems are more complex. I think a lot about what problems you're solving and what answers you're looking at are more about the stage of your organization. Then really about your level within the org, so now, as a head of the customer analytics team. It's about training my team members to be out there listening and learning and asking the right question, but some of the questions are still the same that we were answering that Ann Taylor that we're answering that Kendra Scott and that really has to do with the fact that we're still a relatively young company.


Alison Hartsoe: [13:31] Are you asking the same questions over and over again at a certain cadence in order to kind of take the temperature of when the market changes?


Megan Kohout: [13:39] Oh, that's an interesting question. I think that no matter what, you have to respond and listen to what's happening in the organization at any given time. And so, sometimes you will ask questions every six months, but other times you might answer it one, and that's kind all that you need. So it really is about being really in tune with what's going on in the business. And I think that's my role as the leader of the team. And I have a little bit of an advantage there because I run the e-commerce business, so I intimately know what's going on with that business. But I think that's what's more important than kind of setting out a road map that we're going to answer this particular question during any particular cadence.


Alison Hartsoe: [14:17] Got it! I think that what's interesting about that too is, when you have to train your team members to listen and learn, there must be a bit of an art and a science to that. Is that a character assumption?


Megan Kohout: [14:28] Absolutely. Because when you do need someone who has a particular skill set, writing sequel, when they're learning that, someone isn't necessarily learning about how to ask questions and how to gain that information from other team members. So it is a lot of times where you do ask your team members, I spend per se, a lot of time just asking team members what they think about, what are they hearing, what does the data tell them, what would they recommend based off of the data. And there can sometimes be somewhat of a hesitation where people don't want to suggest something that wouldn't be right or suggest something that doesn't make sense for the team that would execute it. But you really just have to kind of learn to say like, it's just recommendation. You're just sharing it with me. I'm not going to let you suggest anything that would be offensive to anyone, and in that trust have them trust me, that's, that's my job to help make sure the work that they produce is good, but that I want them and need them to think about those things.


Alison Hartsoe: [15:20] You know, that almost sounds like you have a certain kind of formula of looking at people before you hire them that, you know, maybe you're looking for certain traits. What makes a good data ambassador? What do you looking for when you bring those folks in?


Megan Kohout: [15:32] Someone who asks a lot of questions, someone who really has an inquiring mind who wants to understand the customer and wants to understand how data can help us better serve the customer. I think that's really the most important thing, and it varies by level as well because if you do need the person who has the sequel skill set, then you really do need to find that skill set. Other times you can learn it. For me personally, I learned sequel when I was working at Amazon because that was how you got the data out of the system, and so you just had to do it. So I think that is like first and foremost someone who has an analytical background but can ask questions about the system.


Alison Hartsoe: [16:09] So they can put two and two together, it's almost a communication role then, would you say?


Megan Kohout: [16:13] there's definitely a part of it that is because how you share this information with other folks. How do you make it digestible, and how do you sit down and explain it to folks? Because that's a large part of the work is that you'll send out information, I still do this today, so what does this say? And it's like, okay, let's sit down and talk about it. And you kind of has to have those discussions.


Alison Hartsoe: [16:33] Let's dig into that a little bit because I think there's an awful lot of human behavior that happens when information is presented. Whether it's something radical like the Summer Weiss reflects or whether it's something that's just, you know, somebody sitting across the table with their preconceived notions. Is there a way, are there techniques that you've discovered to help people become more receptive to the data?


Megan Kohout: [16:58] It can be really hard when you are sharing something that goes against what someone has believed. And so, a lot of times you have to adjust how you speak to people based off of who they are and what their preconceived assumptions were. And I think that it's important really to rely on the data to help you explain that this is the data that I have, this is what it's showing me, and really remain calm and understand that someone might be going through a moment where there they've been saying something that just isn't right. And so I think there's really a level of empathy that's important.


Alison Hartsoe: [17:30] Yeah, and I can't underscore what you just said enough about that level of empathy because sometimes we get so enamored in the data and we're so excited about it. We're like, Yay, I've done my job well. I found a really good nugget or insight, but that also conversely call somebody on the carpet that maybe their job wasn't being done so well, and we have to be empathetic to that. Have you seen that happen?


Megan Kohout: [17:54] No, I think in those cases also what's really important to do is don't do that in a meeting with leadership team. Go and talk with someone in advance. That's a really good strategy to make sure that you're not telling someone off guard in front of others.


Alison Hartsoe: [18:08] Yeah. Is there a way people can be more sensitive to when they are calling somebody, it looks at might not be obvious. If you're sharing your data insights, how can you be more sensitive to whether that's making someone look bad?


Megan Kohout: [18:24] I think that's really the job of the leader of the organization, and also you know, as you started your work based off of this something that you've been hearing then you kind of know if the assumption has been proven or disproven. So I think between those two that's how you can avoid that.


Alison Hartsoe: [18:40] So you start with the assumption, you bring somebody on board, and then you answer their question. That makes sense. How the customer thinks and feels underscores another point about qualitative versus quantitative data? Is a data ambassador more empowered if they have a mixture of these two?


Megan Kohout: [18:57] I think it's really important as you're building out a customer analytics organization to make sure that you have a way to collect both information. And so that can be things as simple as, for example, we're doing focus groups right now. We're, we're bringing often customers into our office to help understand what they're keen about the brand. You can get qualitative information and surveys are another great way to do that. Survey monkey has come so far as a tool, and you can create surveys that have your own background and look brand right, and really quickly gain information about your customers. And so I think that sometimes when you're telling that data analytics story, it helps to support it using that information that you're getting from customers. For example, one of the big things are always looking at from a customer analytics perspective of is retention rates of customers. So if retention rates are changing, you can see that in the data, but that doesn't help when you're talking to the organization. You need to be helping to understand why our retention rates changing, and you really need the qualitative feedback to back that up so that you're not just presenting a problem.


Alison Hartsoe: [20:03] Is that something that a more junior data ambassador might be inclined to do? And I noticed when you were talking about your role of bringing through their recommendations, are they oftentimes coming in, presenting the problem instead of presenting a fully baked solution?


Megan Kohout: [20:18] There's a lot of times with like, here's what the data says, and then you have to push your team to say, so now, what else do we need to learn, and what are the recommendations based off of this? On my team, in particular, I actually have one person who's dedicated more to the qualitative work and then one person who's really in the sequel, that works for us. Sometimes you can have one person do both or how people that work across both areas, so it doesn't necessarily need to be one way or the other, but I do think that you need to think about making sure you have that qualitative component available.


Alison Hartsoe: [20:52] That makes sense.


Megan Kohout: [20:53] You know, I think just overall the analytics role is so valuable, and I think something that I've always thought about in my career where I'm like, okay, I really nailed this. I really figured this out is when you start to hear other people in the organization repeat the facts that you've shared, and there's nothing more proud than where you're in at a conference, and your CEO is talking, and he's sharing the information that you've shared with him. And I think that that's what's so powerful and so fun and so rewarding about becoming that data ambassador.


Alison Hartsoe: [21:24] Oh I love that, that such a great thing to say. That's the recognition that everybody seeks, and when your ideas are echoed and brought through the organization is a really beautiful moment. That's fantastic Megan. Thank you. If I've decided I want to be more of a data ambassador inside my company. What are the kinds of, you know, first, second, third steps I should be doing? And like you said before, maybe it's within the context of the maturity of my company. Maybe it's in the context of the rank of my role. How should I start?


Megan Kohout: [21:56] Assuming there's an organization out there where it's like we aren't doing any customer analytics work. I think that is, or even, actually as an example, when I was at Amazon, I was part of my habit which was a fashion flash sale startup within Amazon, and I was there for my MBA internship, and we hit the brand had literally launched two months ago. And I had, I was like, you might want to put together a customer profile, and someone is like, no, you don't need to do that. Don't worry about that. No, I'm just going to go and do it. And so, what I did was just get access to the data, and the data you need access to start doing this is really transactional information that has a customer identifier. If your company has a customer id, great. If you just got an email address that's also great, and get the data and start playing around with it. You know you can take a sample of the data in Microsoft Access. You can use a lot of data there and then apply Keith Lens and really roll it up in different ways, and just start producing something. I mean I think that's really the key way to get started is really to start showing people fact then they'll likely want more,


Alison Hartsoe: [22:58] But I think it's interesting you didn't say, go buy a CDP and land all the data together and then producing insights. It sounds like you're really advocating for, use what you have. Data's not perfect.


Megan Kohout: [23:12] Data's not perfect and basic transactional information is really I would say probably 80% of the analytics that I do is from basic transactional information that has some sort of customer identifier. So long if you have that you can start to do a lot with it.


Alison Hartsoe: [23:27] What are some of the things that you think of are immediate go to's for a person who wanted to be this data ambassador, what are some immediate analyses they might look at in the transaction to help them make a dent in how the organization's thinking?


Megan Kohout: [23:41] I think that analysis about showing people the percentage of customers who've only spent that one time hugely valuable, understanding the percentage of new and existing customers, the time between transaction, and then common bundles within an order.


Alison Hartsoe: [23:55] Great Advice. Okay. Once I have that data, then what should I do?


Megan Kohout: [24:00] I found that there are always those people who love data and love those findings, and you have to find those sponsors within the organization. And so, I think you find those people, and you share that information with them, or you think about with this information that I have who could this most impact. So as an example around the retention rates if retention rates are falling. Well, that might be something that is solved by communicating to customers. So who are the people within my organization who are communicating, that might be an e-commerce team that sending out emails, that might be a stores team that has sales associates that could reach out to customers, go to those people who could act on the recommendations that you have based on the data.


Alison Hartsoe: [24:39] Got It. And then what?


Megan Kohout: [24:41] Follow up with them, share it, follow up with them, start showing trend data over time, and see if you can make a change there. One of the things is that it can be hard, and I think that having worked in this type of role in multiple organizations using the same approach, you kind of get things done and get your data out there doesn't always work. So sometimes you kind of feel like you're throwing spaghetti at the wall, to see what sticks. But it's that persistence that's really important.


Alison Hartsoe: [25:06] Yes, you're absolutely right. I think oftentimes there's an adoption curve. You've been looking at the data, you're getting familiar with it, you're trusting it more and more, and then you give it to somebody who you expect to act on it, and it's like the first time they've ever tasted the data, and it takes them a while to get used to it, to trust it, to get to know it. And I wonder if there is a soft selling and not everybody's this way. You know like some people like new food, they jump in, they'll eat anything right away, but some people really need to get like my kids with the vegetables, right. They need to get used to that.


Megan Kohout: [25:40] It can be really challenging. If I think back about my time at Ann Taylor, what I really benefited from, I started off by just doing the analytics, and then took on direct mail, but doesn't mean I forgot about what I learned when I was doing the analytics. So I was there creating mailers that would say, okay, if someone got a suit, you knew they were going to buy a suit again, let's make sure we've got a direct mail piece ready to go once someone bought that suit. So I was lucky in that case that I was in a role where I could apply my learning. And so that's what I tried. I think where I'm in an actually really advantageous place to be leading e-commerce and customer analytics is because I know what those customer analytics are, and I help embed those analytics into our e-commerce business. Even though E-COMMERCE, a lot of times the focus is on traffic conversion, getting people down the funnel, there's actually a whole other funnels to think about the customer funnel, and I'm able to embed that into the natural operations of our organization.


Alison Hartsoe: [26:38] A great way to kind of come alongside and then be running alongside with your partners instead of presenting information that might take them on a left turn. And the mention of the sponsor within the organization, I want to push on that just a little bit because I think it can't be underestimated, but sometimes there are different types of sponsors. There are people who basically give you air cover, and then there are people who are supposed to act on your information. Do you find that the relationship between these two people is different? Is it more important to have one kind of sponsor than another?


Megan Kohout: [27:11] I don't know it it's more important to have one than the other, but I do think it's important to make sure that you're thinking about both sides of it because the one person can help influence. So if you're a junior team member, and you have really cool information, and you have a senior member whose the sponsor, they can also help make sure that someone else whose senior is listening and acting on your data, so I think it's really important to have both, but sometimes that sponsor can really help you out in ways that you might not be able to do.


Alison Hartsoe: [27:36] That makes sense. Megan, is there anything that you would want more from your role in being a data ambassador within the company? Now once you become really good at this, good, successful, where do you go?


Megan Kohout: [27:51] As we look at different companies, we're really starting to see a chief customer officer role. And I think understanding the customer and understanding the analytics has a really good place to go in your career is kind of like a final destination. I also think that it's really important that if you're in this role, being able to show that it can help guide how you would make decisions other places. So for me, I started off doing analytics, but then I was able to show how the analytics could help me do different areas, and so it enabled me to continue to grow and change into other areas, but still always go back to this customer analytics area that I love.


Alison Hartsoe: [28:30] And when you're talking about other areas, are you talking about not just marketing but maybe into supply chain or operations? Is that right?


Megan Kohout: [28:39] I think really at any areas even E-commerce isn't necessarily a marketing area. It is about building up the website and figuring out a plan and general management. And so I think that it's really that customer focus that goes well with that kind of general management of a business area.


Alison Hartsoe: [28:53] So you're prepping yourself to think like a general manager of the business. I think that's a fantastic role for somebody who's naturally entrepreneurial, naturally thinks about the business as a whole. Absolutely. Fantastic. Well, Megan, this has been really nice. If people want to get in touch with you and they have questions about becoming a data ambassador, what's the best way for them to reach you?


Megan Kohout: [29:14] Linkedin is a perfect way to reach me.


Alison Hartsoe: [29:16] Okay, and do you want to spell the way your last name is so that its clear to folks in case they're looking for you?


Megan Kohout: [29:22] Sure, that would be really helpful. So, my name is Megan, m e g a n, and then my last name is Kohout, k o h o u t.


Alison Hartsoe: [29:31] Excellent. Thanks for sharing the spelling of your name, Megan. I'm sure that'll be helpful. And if people want to go to the website, where should they go and how can they check out the great products that Kendra Scott has?


Megan Kohout: [29:42] Absolutely. So our website is, and through June 30th for listeners of this podcast will offer 20% off your full price purchase of $100 or more that does exclude our fine jewelry product with the code ambition.


Alison Hartsoe: [29:57] Wow, that's awesome, I'm, going to go do that. I literally saw this Great, it was like a pendant and earrings matte, I'm going to run right over and pick up on your site. And I can tell you from firsthand experience, and Megan is awesome, and she's got lots of great advice and a very talented person in the industry. I'm sure Kendra Scott is thrilled to have you, and we'll eminently be promoting you to chief customer officer, right, so as always, links to everything we discussed Megan, thank you for joining us today.


Megan Kohout: [30:35] Thank you so much for having me.


Alison Hartsoe: [30:37] Remember when you use your data effectively, you can build customer equity. It's not magic. It's just a very specific journey that you can follow to get results. Thank you for joining today's show. This is your host, Allison Hartsoe, and I have two gifts for you. First, I've written a guide for the customer centric Cmo, which contains some of the best ideas from this podcast, and you can receive it right now. Simply text, ambitiondata, one word to, three, one, nine, nine, six, (31996) and after you get that white paper, you'll have the option for the second gift, which is to receive The Signal. Once a month. I put together a list of three to five things I've seen that represent customer equity signal not noise, and believe me, there's a lot of noise out there. Things I include could be smart tools. I've run across, articles I've shared cool statistics, or people and companies I think are making amazing progress as they build customer equity. I hope you enjoy the CMO guide and The Signal. See you next week on the Customer Equity Accelerator.



Key Concepts:  Customer Lifetime Value, Marketing, Digital Data, Customer Centricity, Long-Term Customer Value, Marketing Leaders, Analytics, Creativity, Product Development, Audience Research

Who Should Listen:  CAOs, CCOs, CSOs, CDOs, Digital Marketers, Business Analysts, C-suite professionals, Entrepreneurs, eCommerce, Data Scientists, Analysts, CMOs, Customer Insights Leaders, CX Analysts, Data Services Leaders, Data Insights Leaders, SVPs or VPs of Marketing or Digital Marketing, SVPs or VPs of Customer Success, Customer Advocates, Product Managers, Product Developers

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